20,091 research outputs found
Born to trade: a genetically evolved keyword bidder for sponsored search
In sponsored search auctions, advertisers choose a set of keywords based on products they wish to market. They bid for advertising slots that will be displayed on the search results page when a user submits a query containing the keywords that the advertiser selected. Deciding how much to bid is a real challenge: if the bid is too low with respect to the bids of other advertisers, the ad might not get displayed in a favorable position; a bid that is too high on the other hand might not be profitable either, since the attracted number of conversions might not be enough to compensate for the high cost per click.
In this paper we propose a genetically evolved keyword bidding strategy that decides how much to bid for each query based on historical data such as the position obtained on the previous day. In light of the fact that our approach does not implement any particular expert knowledge on keyword auctions, it did remarkably well in the Trading Agent Competition at IJCAI2009
Pricing average price advertising options when underlying spot market prices are discontinuous
Advertising options have been recently studied as a special type of
guaranteed contracts in online advertising, which are an alternative sales
mechanism to real-time auctions. An advertising option is a contract which
gives its buyer a right but not obligation to enter into transactions to
purchase page views or link clicks at one or multiple pre-specified prices in a
specific future period. Different from typical guaranteed contracts, the option
buyer pays a lower upfront fee but can have greater flexibility and more
control of advertising. Many studies on advertising options so far have been
restricted to the situations where the option payoff is determined by the
underlying spot market price at a specific time point and the price evolution
over time is assumed to be continuous. The former leads to a biased calculation
of option payoff and the latter is invalid empirically for many online
advertising slots. This paper addresses these two limitations by proposing a
new advertising option pricing framework. First, the option payoff is
calculated based on an average price over a specific future period. Therefore,
the option becomes path-dependent. The average price is measured by the power
mean, which contains several existing option payoff functions as its special
cases. Second, jump-diffusion stochastic models are used to describe the
movement of the underlying spot market price, which incorporate several
important statistical properties including jumps and spikes, non-normality, and
absence of autocorrelations. A general option pricing algorithm is obtained
based on Monte Carlo simulation. In addition, an explicit pricing formula is
derived for the case when the option payoff is based on the geometric mean.
This pricing formula is also a generalized version of several other option
pricing models discussed in related studies.Comment: IEEE Transactions on Knowledge and Data Engineering, 201
Competition and Cooperation Analysis for Data Sponsored Market: A Network Effects Model
The data sponsored scheme allows the content provider to cover parts of the
cellular data costs for mobile users. Thus the content service becomes
appealing to more users and potentially generates more profit gain to the
content provider. In this paper, we consider a sponsored data market with a
monopoly network service provider, a single content provider, and multiple
users. In particular, we model the interactions of three entities as a
two-stage Stackelberg game, where the service provider and content provider act
as the leaders determining the pricing and sponsoring strategies, respectively,
in the first stage, and the users act as the followers deciding on their data
demand in the second stage. We investigate the mutual interaction of the
service provider and content provider in two cases: (i) competitive case, where
the content provider and service provider optimize their strategies separately
and competitively, each aiming at maximizing the profit and revenue,
respectively; and (ii) cooperative case, where the two providers jointly
optimize their strategies, with the purpose of maximizing their aggregate
profits. We analyze the sub-game perfect equilibrium in both cases. Via
extensive simulations, we demonstrate that the network effects significantly
improve the payoff of three entities in this market, i.e., utilities of users,
the profit of content provider and the revenue of service provider. In
addition, it is revealed that the cooperation between the two providers is the
best choice for all three entities.Comment: 7 pages, submitted to one conferenc
User Satisfaction in Competitive Sponsored Search
We present a model of competition between web search algorithms, and study
the impact of such competition on user welfare. In our model, search providers
compete for customers by strategically selecting which search results to
display in response to user queries. Customers, in turn, have private
preferences over search results and will tend to use search engines that are
more likely to display pages satisfying their demands.
Our main question is whether competition between search engines increases the
overall welfare of the users (i.e., the likelihood that a user finds a page of
interest). When search engines derive utility only from customers to whom they
show relevant results, we show that they differentiate their results, and every
equilibrium of the resulting game achieves at least half of the welfare that
could be obtained by a social planner. This bound also applies whenever the
likelihood of selecting a given engine is a convex function of the probability
that a user's demand will be satisfied, which includes natural Markovian models
of user behavior.
On the other hand, when search engines derive utility from all customers
(independent of search result relevance) and the customer demand functions are
not convex, there are instances in which the (unique) equilibrium involves no
differentiation between engines and a high degree of randomness in search
results. This can degrade social welfare by a factor of the square root of N
relative to the social optimum, where N is the number of webpages. These bad
equilibria persist even when search engines can extract only small (but
non-zero) expected revenue from dissatisfied users, and much higher revenue
from satisfied ones
Lock-in & Break-out from Technological Trajectories: Modeling and policy implications
Arthur [1,2] provided a model to explain the circumstances that lead to
technological lock-in into a specific trajectory. We contribute substantially
to this area of research by investigating the circumstances under which
technological development may break-out of a trajectory. We argue that for this
to happen, a third selection mechanism--beyond those of the market and of
technology--needs to upset the lock-in. We model the interaction, or mutual
shaping among three selection mechanisms, and thus this paper also allows for a
better understanding of when a technology will lock-in into a trajectory, when
a technology may break-out of a lock-in, and when competing technologies may
co-exist in a balance. As a system is conceptualized to gain a (third) degree
of freedom, the possibility of bifurcation is introduced into the model. The
equations, in which interactions between competition and selection mechanisms
can be modeled, allow one to specify conditions for lock-in, competitive
balance, and break-out
Two Narratives of Platform Capitalism
Mainstream economists tend to pride themselves on the discipline\u27s resemblance to science. But growing concerns about the reproducibility of economic research are undermining that source of legitimacy. These concerns have fueled renewed interest in another aspect of economic thought: its narrative nature. When presenting or framing their work, neoliberal economists tend to tell stories about supply and demand, unintended consequences, and transaction costs in order to justify certain policy positions. These stories often make sense, and warn policymakers against simplistic solutionism
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Contextual advertising
Contextual advertising entails the display of relevant ads based on the content that consumers view, exploiting the potential that consumers' content preferences are indicative of their product preferences. This paper studies the strategic aspects of such advertising, considering an intermediary who has access to a content base, sells advertising space to advertisers who compete in the product market, and provides the targeting technology. The results show that contextual targeting impacts advertiser profit in two ways: First, advertising through relevant content topics helps advertisers reach consumers with a strong preference for their product. Second, heterogeneity in consumers' content preferences can be leveraged to reduce product market competition, especially when competition is intense. The intermediary has incentives to strategically design its targeting technology, sometimes at the cost of the advertisers. When product market competition is moderate, the intermediary offers accurate targeting such that the consumers see the most relevant ads. When competition is high, the intermediary lowers the targeting accuracy such that the consumers see less relevant ads. Doing so intensifies competition and encourages advertisers to bid for multiple content topics in order to prevent their competitors from reaching consumers. In some cases, this may lead to an asymmetric equilibrium where one advertiser bids high even for the content topic that is more relevant to its competitor. © 2012 INFORMS
A Non-cooperative Game-Theoretic Framework for Sponsoring Content in the Internet Market
Data traffic demand over the Internet is increasing rapidly, and it is changing the pricing model between internet service providers (ISPs), content providers (CPs) and end users. One recent pricing proposal is sponsored data plan, i.e., when CP negotiates with the ISP on behalf of the users to remove the network subscription fees so as to attract more users and increase the number of advertisements. As such, a key challenge is how to provide proper sponsorship in the situation of complex interactions among the telecommunication actors, namely, the advertisers, the content provider, and users. To answer those questions, we explore the potential economic impacts of this new pricing model by modeling the interplay among the advertiser, users, and the CPs in a game theoretic framework. The CP may have either a subscription revenue model (charging end-users) or an advertisement revenue model (charging advertisers). In this work, we design and analyze the interaction among CPs having an advertisement revenue as a non-cooperative game, where each CP determines the proportion of data to sponsor and a level of credibility of content. In turn, the end-users demand for the content of a CP depends not only on their strategies but also upon those proposed by all of its competitors. Through rigorous mathematical analysis, we prove the existence and uniqueness of the Nash equilibrium. Based on the analysis of the game properties, we propose an iterative algorithm, which guarantees to converge to the Nash equilibrium point in a distributed manner. Numerical investigation shows the convergence of a proposed algorithm to the Nash equilibrium point and corroborates the fact that sponsoring content may improve the CPs outcome
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